mlbeta

The data from which the estimate is to be computed.

Optional starting parameter values for the minimization.
Passed to the <code>stats::nlm</code> function.

start

Whether a dedicated <code>gradient</code> or <code>hessian</code> should be
passed to <code>stats::nlm</code>.

type

Uses <code>stat::nlm</code> to estimate the parameters of the Beta distribution.

internal

Handles univariate non-parametric density estimation with
parametric starts and asymmetric kernels in a simple and flexible way.
Kernel density estimation with parametric starts involves fitting a
parametric density to the data before making a correction with kernel
density estimation, see Hjort & Glad (1995) <doi:10.1214/aos/1176324627>.
Asymmetric kernels make kernel density estimation more efficient on bounded
intervals such as (0, 1) and the positive half-line. Supported asymmetric
kernels are the gamma kernel of Chen (2000) <doi:10.1023/A:1004165218295>,
the beta kernel of Chen (1999) <doi:10.1016/S0167-9473(99)00010-9>, and the
copula kernel of Jones & Henderson (2007) <doi:10.1093/biomet/asm068>.
User-supplied kernels, parametric starts, and bandwidths are supported.

mlbeta: Estimates the parameter of the Beta distribution using maximum likelihood

Description

Usage

Arguments

Value

Details